Partition based optimization for updating sample allocation strategy using lookahead

Simulation models typically describe complicated systems with no closed-form analytic expression. To optimize these complex models, general “black-box” optimization techniques must be used. To confront computational limitations, Optimal Computational Budget Allocation (OCBA) algorithms have been developed in order to arrive at the best solution relative to a finite amount of resources primarily for a finite design space. In this paper we extend the OCBA methodology for partition based random search on a continuous domain using a lookahead approximation on the probability of correct selection. The algorithm uses the approximation to determine the order of dimensional-search and a stopping criterion for each dimension. The numerical experiments indicate that the lookahead OCBA algorithm improves the allocation of computational budget on asymmetrical functions while preserving asymptotic performance of the general algorithm.